Most of currently available security surveillance systems have the problems of low image resolution and relatively small target size. The low image resolution results in unclear images that is unidentifiable and could not be effectively improved even through digital image processing. To solve these problems existing in the conventional security surveillance systems, there is development of Automatic Target Tracking System, in which a first image-capturing device is used to get a full-scene image, from which a target object is located through image comparison using a processor; and then, a second image-capturing device is driven to get a local image of target area, so as to effectively obtain the target object image with much improved quality. While the above-described Automatic Target Tracking System effectively improves the image quality, it has many other problems in the practical use. For instance, in the full-scene image, targets such as people, cars, and the like in different zones have different degrees of importance; swaying trees, flags, shadows, etc. form external environmental interferences; and targets are located at different distances from the image-capturing device. In brief, there are complicate background and multiple targets included in the full-scene image at the same time. Moreover, the current image processing technique does not ensure capturing the most important target object.
Another problem with the conventional security surveillance systems is that all the image data stored are full-scene image data, which is very big in volume and adversely shortens the recording time available from one storage device. Currently, a common way to extend the surveillance recording time is to use black-white and/or decrease resolution.
Therefore, it is an important issue to provide a target object detection system and method that is able to effectively locate the target object of interest and determine the degree of importance, and therefore improve the recording quality and efficiency of the surveillance system.